Home win
16.52%
Bologna
ITA1 · Serie A · Match Analysis
Bologna vs
Inter Milan
Model-based match probabilities derived from multi-season xG data, team strength, venue performance, recent form, Elo ratings and league context.
Model output
Match probabilities.
Probabilities are model estimates, not certainties or betting guarantees.
Draw
22.26%
X
Away win
61.22%
Inter Milan
Score model
Most likely scorelines.
Exact-score probabilities from the Poisson + Dixon-Coles goal model.
Additional scorelines
0:36.82%
0:05.38%
2:25.07%
2:14.82%
1:43.66%
Team context
Strength and recent form.
Venue-adjusted team strength, Elo rating and recent xG performance.
Home
Bologna
Elo1515.0
Overall attack1.21
Overall defense1.12
Home attack1.36
Home defense1.14
Form signal+0.088
Last 5 xGF / xGA1.27 / 1.20
Last 10 xGF / xGA1.26 / 1.15
Last 10 xGD+0.12
League statusSTABLE · from ITA1 2025/26
Away
Inter Milan
Elo1679.8
Overall attack2.03
Overall defense0.98
Away attack1.84
Away defense1.08
Form signal+1.771
Last 5 xGF / xGA3.03 / 1.14
Last 10 xGF / xGA2.67 / 1.12
Last 10 xGD+1.55
League statusSTABLE · from ITA1 2025/26
Elo difference
-164.8
Elo is one input among several and does not determine the final 1X2 probability on its own.
Head-to-head
Historical matchup context.
H2H values provide context and are not used as a standalone prediction.
Goal distribution
Total goals profile.
The model's probability distribution for the total number of goals.
05.38%
111.80%
222.45%
322.35%
417.45%
510.90%
65.67%
7+4.01%
Technical detail
How the model reached the estimate.
Detailed diagnostics are kept below the headline probabilities so the public report remains readable.
League baseline and model setup
LeagueITA1
Loaded teams27
Loaded historical matches1580
Target season2026/27
Previous seasons in history4
Season window2022/23 → 2023/24 → 2024/25 → 2025/26 → 2026/27
League tierTier 1
Historical league matches1580
xG matches1580 / 1580
Effective xG matches799.14
Observed home xG1.37
Observed away xG1.17
Model home xG baseline1.36
Model away xG baseline1.18
Total league xG baseline2.54
Home-advantage weight0.875
Model configdata/models/ITA1.json
Team attack half-life240.0 days
Team defense half-life240.0 days
Attack strength weight1.050
Defense strength weight1.081
Venue prior matches3.31
DistributionPoisson + Dixon-Coles
Dixon-Coles rho-0.106 (effective -0.103)
Dixon-Coles decay0.034
Goal overdispersion0.000
Implied goal variance H / A1.02 / 2.10
Relative strengths and form diagnostics
Bologna
Home attack strength0.990
Home defense weakness0.967
Base lambda home1.10
Attack form xGF level1.268
Defense form xGA level (lower is better)1.180
Opponent-adjusted attack residual+0.012
Opponent-adjusted defense residual (higher is better)+0.108
Opponent-adjustment weight0.000
Model attack signal-0.004
Model defense signal+0.091
Opponent blend0.000
Form matches 5 / 105 / 10
Last 5 xGF / xGA / xGD1.27 / 1.20 / +0.07
Last 10 xGF / xGA / xGD1.26 / 1.15 / +0.12
Inter Milan
Away attack strength1.451
Away defense weakness0.831
Base lambda away1.69
Attack form xGF level2.904
Defense form xGA level (lower is better)1.133
Opponent-adjusted attack residual+1.546
Opponent-adjusted defense residual (higher is better)+0.058
Opponent-adjustment weight0.000
Model attack signal+1.500
Model defense signal+0.138
Opponent blend0.000
Form matches 5 / 105 / 10
Last 5 xGF / xGA / xGD3.03 / 1.14 / +1.89
Last 10 xGF / xGA / xGD2.67 / 1.12 / +1.55
Historical data coverage
Bologna
Total matches158
Home / away matches79 / 79
xG matches158 / 158
xG home / away coverage79 / 79 · 79 / 79
Effective total31.77
Effective attack / defense31.77 / 31.77
Effective home matches15.94
Effective home attack / defense15.94 / 15.94
Inter Milan
Total matches158
Home / away matches80 / 78
xG matches158 / 158
xG home / away coverage80 / 80 · 78 / 78
Effective total31.72
Effective attack / defense31.72 / 31.72
Effective away matches15.14
Effective away attack / defense15.14 / 15.14
Historical data coverage
96.17%
This measures usable historical evidence. It is not the probability that the prediction is correct.
Validation
Historical model performance.
Leakage-safe backtesting compares the fitted model with a league baseline over 1560 historical matches.
Positive improvement means the model performed better than the league baseline.
Total goals log-loss vs baseline+0.63%
1X2 log-loss vs baseline+9.07%
BTTS Brier vs baseline+0.37%
Over 2.5 Brier vs baseline+1.34%
Full backtest metrics
Model
MAE total goals1.2171
Exact score log-loss2.7695
Total goals Brier0.8119
Total goals log-loss1.8027
1X2 Brier0.5899
1X2 log-loss0.9879
BTTS Brier0.2496
Over 1.5 Brier0.1909
Over 2.5 Brier0.2463
Over 3.5 Brier0.1841
League baseline
MAE total goals1.2442
Exact score log-loss2.8998
Total goals Brier0.8136
Total goals log-loss1.8143
1X2 Brier0.6583
1X2 log-loss1.0864
BTTS Brier0.2505
Over 1.5 Brier0.1925
Over 2.5 Brier0.2497
Over 3.5 Brier0.1866
Calibration diagnostics
Home win · ECE 2.51%
0- 10% n=22 avg= 8.3% actual= 9.1% 10- 20% n=177 avg= 15.9% actual= 15.8% 20- 30% n=282 avg= 25.2% actual= 25.5% 30- 40% n=333 avg= 35.2% actual= 33.6% 40- 50% n=297 avg= 44.7% actual= 48.1% 50- 60% n=218 avg= 54.8% actual= 51.8% 60- 70% n=161 avg= 64.5% actual= 70.8% 70- 80% n=59 avg= 73.8% actual= 79.7% 80- 90% n=11 avg= 83.1% actual= 63.6%
Draw · ECE 1.65%
0- 10% n=1 avg= 8.3% actual= 0.0% 10- 20% n=94 avg= 17.1% actual= 21.3% 20- 30% n=1059 avg= 26.5% actual= 25.3% 30- 40% n=406 avg= 31.5% actual= 33.7%
Away win · ECE 3.79%
0- 10% n=73 avg= 7.7% actual= 6.8% 10- 20% n=308 avg= 15.0% actual= 12.3% 20- 30% n=368 avg= 25.0% actual= 22.0% 30- 40% n=343 avg= 34.6% actual= 30.6% 40- 50% n=208 avg= 44.6% actual= 53.4% 50- 60% n=160 avg= 54.3% actual= 53.8% 60- 70% n=74 avg= 63.6% actual= 70.3% 70- 80% n=25 avg= 73.6% actual= 76.0% 80- 90% n=1 avg= 83.8% actual= 0.0%
BTTS · ECE 1.92%
30- 40% n=64 avg= 37.9% actual= 45.3% 40- 50% n=823 avg= 45.9% actual= 48.4% 50- 60% n=637 avg= 53.6% actual= 53.1% 60- 70% n=36 avg= 61.8% actual= 58.3%
Over 2.5 · ECE 2.49%
20- 30% n=17 avg= 28.1% actual= 23.5% 30- 40% n=300 avg= 36.9% actual= 40.0% 40- 50% n=705 avg= 44.9% actual= 47.1% 50- 60% n=455 avg= 53.9% actual= 51.6% 60- 70% n=78 avg= 63.4% actual= 60.3% 70- 80% n=5 avg= 71.5% actual= 80.0%
Walk-forward - latest periods
| Period | N | Model 1X2 LL | Baseline 1X2 LL | Improvement |
|---|---|---|---|---|
| 2025-09 | 30 | 0.9825 | 1.0882 | +9.71% |
| 2025-10 | 40 | 1.0491 | 1.1060 | +5.14% |
| 2025-11 | 39 | 1.0141 | 1.0922 | +7.15% |
| 2025-12 | 37 | 1.0639 | 1.0763 | +1.15% |
| 2026-01 | 58 | 0.9365 | 1.0986 | +14.75% |
| 2026-02 | 40 | 0.9507 | 1.0931 | +13.03% |
| 2026-03 | 36 | 0.9894 | 1.0574 | +6.43% |
| 2026-04 | 40 | 1.0026 | 1.0882 | +7.87% |
| 2026-05 | 40 | 1.1007 | 1.1080 | +0.66% |
| 2026-08 | 20 | 0.7777 | 1.0805 | +28.03% |
| 2026-09 | 30 | 1.0237 | 1.0700 | +4.33% |
| 2026-10 | 10 | 0.8909 | 1.0354 | +13.96% |
Football is data.
NILYTIX turns match data into transparent model probabilities and measurable analytical signals.